{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# pandas 进阶修炼 ｜早起Python\r\n",
    "<br>\r\n",
    "\r\n",
    "**本习题由公众号【早起Python & 可视化图鉴】 原创，转载及其他形式合作请与我们联系（微信号`sshs321`)，未经授权严禁搬运及二次创作，侵权必究！**\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "本习题基于 `pandas` 版本 `1.1.3`，所有内容应当在 `Jupyter Notebook` 中执行以获得最佳效果。\r\n",
    "\r\n",
    "不同版本之间写法可能会有少许不同，如若碰到此情况，你应该学会如何自行检索解决。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 1 - 数据加载与存储 "
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "是的，常常被忽略的「<font color=#E36C07>**数据加载与存储**</font>」也大有门道且值得作为本套习题的开门之章。\r\n",
    "\r\n",
    "在一次数据分析的过程中，你可能只会读取或存储一两次数据集。\r\n",
    "\r\n",
    "**但若能灵活掌握各项设置，在读取阶段就将数据筛选、匹配、格式指定等操作完成，有时会为我们节省大量时间。**\r\n",
    "\r\n",
    "在本节习题中，我将 pandas 数据分析中常见的数据读取与存储操作进行整理。\r\n",
    "\r\n",
    "<font color=#E36C07>**既可以用于巩固、学习各种操作，也可以作为速查手册使用**</font>。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 初始化\n",
    "\n",
    "<br>\n",
    "\n",
    "该 `Notebook` 版本为**纯习题版**\n",
    "\n",
    "如果需要答案或者提示，可以微信搜索公众号「早起Python」获取！"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 1-1 数据读取"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 1 读取 Excel 文件\n",
    "\n",
    "<br>\n",
    "\n",
    "- 读取当前目录下 `某招聘网站数据.csv` 文件\n",
    "\n",
    "- 读取当前目录下 `TOP250.xlsx` 文件\n",
    "\n",
    "**注意**：使用 `pandas` 读取 `CSV` 与 读取 `xlsx` 格式的 `Excel` 文件方法大致相同\n",
    "\n",
    "因此接下来与 `Excel` 相关的操作均以 `CSV` 格式进行出题。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import pandas as pd\r\n",
    "data = pd.read_csv(\"某招聘网站数据.csv\")\r\n",
    "data = pd.read_excel(\"TOP250.xlsx\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 2 读取 Excel 文件｜指定位置\r\n",
    "\r\n",
    "在大多数情况下，我们会将 `notebook` 和数据源文件放在同一个目录（文件夹下），这样直接使用`pd.read_xxx(\"文件名\")`即可成功读取。\r\n",
    " \r\n",
    "但有时需要读取的文件和 `notebook` 不在同一个目录下，这时可以使用绝对路径或者相对本 `notebook` 的路径。\r\n",
    "\r\n",
    "现在请读取本套习题中第二章节下的数据，即 `2 - 个性化显示设置/data.csv`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "data = pd.read_csv('../2 - 个性化显示设置\\data.csv')\r\n",
    "data[:5]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <th>thirdType</th>\n",
       "      <th>...</th>\n",
       "      <th>plus</th>\n",
       "      <th>pcShow</th>\n",
       "      <th>appShow</th>\n",
       "      <th>deliver</th>\n",
       "      <th>gradeDescription</th>\n",
       "      <th>promotionScoreExplain</th>\n",
       "      <th>isHotHire</th>\n",
       "      <th>count</th>\n",
       "      <th>aggregatePositionIds</th>\n",
       "      <th>famousCompany</th>\n",
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       "      <td>5204912</td>\n",
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       "      <td>50735</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>电商</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['年终奖金', '做五休二', '六险一金', '子女福利']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>建模</td>\n",
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       "      <td>数据分析</td>\n",
       "      <td>100125</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['节日礼物', '年底双薪', '股票期权', '带薪年假']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6496141</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>26564</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>电商</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['生日趴', '每月腐败基金', '每月补贴', '年度旅游']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>[]</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6467417</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>29211</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>物流丨运输</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训', '免费班车', '专项奖金', '岗位晋升']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
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       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
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       "   positionId positionName  companyId companySize industryField financeStage  \\\n",
       "0     6802721         数据分析     475770     50-150人      移动互联网,电商           A轮   \n",
       "1     5204912         数据建模      50735    150-500人            电商           B轮   \n",
       "2     6877668         数据分析     100125     2000人以上    移动互联网,企业服务         上市公司   \n",
       "3     6496141         数据分析      26564   500-2000人            电商        D轮及以上   \n",
       "4     6467417         数据分析      29211     2000人以上         物流丨运输         上市公司   \n",
       "\n",
       "                    companyLabelList  firstType secondType thirdType  ...  \\\n",
       "0   ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "1   ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类       数据开发        建模  ...   \n",
       "2   ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "3  ['生日趴', '每月腐败基金', '每月补贴', '年度旅游']  开发|测试|运维类       数据开发      数据分析  ...   \n",
       "4   ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "\n",
       "  plus pcShow appShow deliver gradeDescription promotionScoreExplain  \\\n",
       "0  NaN      0       0       0              NaN                   NaN   \n",
       "1  NaN      0       0       0              NaN                   NaN   \n",
       "2  NaN      0       0       0              NaN                   NaN   \n",
       "3  NaN      0       0       0              NaN                   NaN   \n",
       "4  NaN      0       0       0              NaN                   NaN   \n",
       "\n",
       "  isHotHire  count aggregatePositionIds famousCompany  \n",
       "0         0      0                   []         False  \n",
       "1         0      0                   []         False  \n",
       "2         0      0                   []         False  \n",
       "3         0      0                   []          True  \n",
       "4         0      0                   []          True  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  3 读取 Excel 文件｜指定行（顺序）\r\n",
    "<br>\r\n",
    "\r\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件的 <font color = '#5F5FFC'>前20行</font>"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "data = pd.read_csv(\"./某招聘网站数据.csv\", header=0, nrows=20)\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "      <td>移动互联网,电商</td>\n",
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       "      <td>['绩效奖金', '带薪年假', '定期体检', '弹性工作']</td>\n",
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       "      <td>150-500人</td>\n",
       "      <td>电商</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['年终奖金', '做五休二', '六险一金', '子女福利']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
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       "      <td>6877668</td>\n",
       "      <td>数据分析</td>\n",
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       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['节日礼物', '年底双薪', '股票期权', '带薪年假']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6496141</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>26564</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>电商</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['生日趴', '每月腐败基金', '每月补贴', '年度旅游']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6467417</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>29211</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>物流丨运输</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训', '免费班车', '专项奖金', '岗位晋升']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6882347</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>94826</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网,社交</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['股票期权', '扁平管理', '五险一金', '岗位晋升']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6841659</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>348784</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['大牛团队', '扁平管理', '年底双薪', '股票期权']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6764018</td>\n",
       "      <td>数据建模工程师</td>\n",
       "      <td>13163</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['绩效奖金', '股票期权', '年底双薪', '专项奖金']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>建模</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6458372</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>34132</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>数据服务,广告营销</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>其他数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6786904</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>13163</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['绩效奖金', '股票期权', '年底双薪', '专项奖金']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>BI工程师</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6804629</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>34132</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>数据服务,广告营销</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6847013</td>\n",
       "      <td>大数据分析工程师(J11108)</td>\n",
       "      <td>55046</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训', '年底双薪', '带薪年假', '岗位晋升']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6763962</td>\n",
       "      <td>数据分析工程师</td>\n",
       "      <td>13163</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['绩效奖金', '股票期权', '年底双薪', '专项奖金']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6804489</td>\n",
       "      <td>资深数据分析师</td>\n",
       "      <td>34132</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>数据服务,广告营销</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6657285</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>7461</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['工程师氛围', '弹性工作', '扁平管理', '上班不打卡']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6882983</td>\n",
       "      <td>产品运营（偏数据分析）</td>\n",
       "      <td>7461</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['工程师氛围', '弹性工作', '扁平管理', '上班不打卡']</td>\n",
       "      <td>运营|编辑|客服类</td>\n",
       "      <td>运营</td>\n",
       "      <td>数据运营</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6486988</td>\n",
       "      <td>资深数据分析师（杭州）</td>\n",
       "      <td>7461</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['工程师氛围', '弹性工作', '扁平管理', '上班不打卡']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5519962</td>\n",
       "      <td>大数据建模总监</td>\n",
       "      <td>50735</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>电商</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['年终奖金', '做五休二', '六险一金', '子女福利']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>建模</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5559894</td>\n",
       "      <td>数据建模专家-杭州-01546</td>\n",
       "      <td>6502</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>信息安全,数据服务</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['技能培训', '股票期权', '绩效奖金', '岗位晋升']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>建模</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6702852</td>\n",
       "      <td>数据分析专家（游戏业务）</td>\n",
       "      <td>593</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,游戏</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['五险一金', '交通补助', '绩效奖金', '节日礼物']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    positionId      positionName  companyId companySize industryField  \\\n",
       "0      6802721              数据分析     475770     50-150人      移动互联网,电商   \n",
       "1      5204912              数据建模      50735    150-500人            电商   \n",
       "2      6877668              数据分析     100125     2000人以上    移动互联网,企业服务   \n",
       "3      6496141              数据分析      26564   500-2000人            电商   \n",
       "4      6467417              数据分析      29211     2000人以上         物流丨运输   \n",
       "5      6882347              数据分析      94826     50-150人      移动互联网,社交   \n",
       "6      6841659              数据分析     348784     50-150人      移动互联网,电商   \n",
       "7      6764018           数据建模工程师      13163   500-2000人         移动互联网   \n",
       "8      6458372            数据分析专家      34132    150-500人     数据服务,广告营销   \n",
       "9      6786904             数据分析师      13163   500-2000人         移动互联网   \n",
       "10     6804629             数据分析师      34132    150-500人     数据服务,广告营销   \n",
       "11     6847013  大数据分析工程师(J11108)      55046     2000人以上    移动互联网,企业服务   \n",
       "12     6763962           数据分析工程师      13163   500-2000人         移动互联网   \n",
       "13     6804489           资深数据分析师      34132    150-500人     数据服务,广告营销   \n",
       "14     6657285             数据分析师       7461     2000人以上          企业服务   \n",
       "15     6882983       产品运营（偏数据分析）       7461     2000人以上          企业服务   \n",
       "16     6486988       资深数据分析师（杭州）       7461     2000人以上          企业服务   \n",
       "17     5519962           大数据建模总监      50735    150-500人            电商   \n",
       "18     5559894   数据建模专家-杭州-01546       6502   500-2000人     信息安全,数据服务   \n",
       "19     6702852      数据分析专家（游戏业务）        593     2000人以上      移动互联网,游戏   \n",
       "\n",
       "   financeStage                     companyLabelList  firstType secondType  \\\n",
       "0            A轮     ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析   \n",
       "1            B轮     ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类       数据开发   \n",
       "2          上市公司     ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析   \n",
       "3         D轮及以上    ['生日趴', '每月腐败基金', '每月补贴', '年度旅游']  开发|测试|运维类       数据开发   \n",
       "4          上市公司     ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类       数据分析   \n",
       "5            B轮     ['股票期权', '扁平管理', '五险一金', '岗位晋升']  产品|需求|项目类       数据分析   \n",
       "6            A轮     ['大牛团队', '扁平管理', '年底双薪', '股票期权']  产品|需求|项目类       数据分析   \n",
       "7          上市公司     ['绩效奖金', '股票期权', '年底双薪', '专项奖金']  开发|测试|运维类       数据开发   \n",
       "8            A轮  ['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']  产品|需求|项目类       数据分析   \n",
       "9          上市公司     ['绩效奖金', '股票期权', '年底双薪', '专项奖金']  开发|测试|运维类       数据开发   \n",
       "10           A轮  ['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']  产品|需求|项目类       数据分析   \n",
       "11         上市公司     ['技能培训', '年底双薪', '带薪年假', '岗位晋升']  开发|测试|运维类       数据开发   \n",
       "12         上市公司     ['绩效奖金', '股票期权', '年底双薪', '专项奖金']  开发|测试|运维类       数据开发   \n",
       "13           A轮  ['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']  开发|测试|运维类       数据开发   \n",
       "14         上市公司   ['工程师氛围', '弹性工作', '扁平管理', '上班不打卡']  产品|需求|项目类       数据分析   \n",
       "15         上市公司   ['工程师氛围', '弹性工作', '扁平管理', '上班不打卡']  运营|编辑|客服类         运营   \n",
       "16         上市公司   ['工程师氛围', '弹性工作', '扁平管理', '上班不打卡']  开发|测试|运维类       数据开发   \n",
       "17           B轮     ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类       数据开发   \n",
       "18        D轮及以上     ['技能培训', '股票期权', '绩效奖金', '岗位晋升']  开发|测试|运维类       数据开发   \n",
       "19        不需要融资     ['五险一金', '交通补助', '绩效奖金', '节日礼物']  开发|测试|运维类       数据开发   \n",
       "\n",
       "   thirdType  ... plus pcShow appShow deliver gradeDescription  \\\n",
       "0       数据分析  ...  NaN      0       0       0              NaN   \n",
       "1         建模  ...  NaN      0       0       0              NaN   \n",
       "2       数据分析  ...  NaN      0       0       0              NaN   \n",
       "3       数据分析  ...  NaN      0       0       0              NaN   \n",
       "4       数据分析  ...  NaN      0       0       0              NaN   \n",
       "5       数据分析  ...  NaN      0       0       0              NaN   \n",
       "6       数据分析  ...  NaN      0       0       0              NaN   \n",
       "7         建模  ...  NaN      0       0       0              NaN   \n",
       "8     其他数据分析  ...  NaN      0       0       0              NaN   \n",
       "9      BI工程师  ...  NaN      0       0       0              NaN   \n",
       "10      数据分析  ...  NaN      0       0       0              NaN   \n",
       "11      数据分析  ...  NaN      0       0       0              NaN   \n",
       "12      数据分析  ...  NaN      0       0       0              NaN   \n",
       "13      数据分析  ...  NaN      0       0       0              NaN   \n",
       "14      数据分析  ...  NaN      0       0       0              NaN   \n",
       "15      数据运营  ...  NaN      0       0       0              NaN   \n",
       "16      数据分析  ...  NaN      0       0       0              NaN   \n",
       "17        建模  ...  NaN      0       0       0              NaN   \n",
       "18        建模  ...  NaN      0       0       0              NaN   \n",
       "19      数据分析  ...  NaN      0       0       0              NaN   \n",
       "\n",
       "   promotionScoreExplain isHotHire  count aggregatePositionIds famousCompany  \n",
       "0                    NaN         0      0                   []         False  \n",
       "1                    NaN         0      0                   []         False  \n",
       "2                    NaN         0      0                   []         False  \n",
       "3                    NaN         0      0                   []          True  \n",
       "4                    NaN         0      0                   []          True  \n",
       "5                    NaN         0      0                   []         False  \n",
       "6                    NaN         0      0                   []         False  \n",
       "7                    NaN         0      0                   []          True  \n",
       "8                    NaN         0      0                   []         False  \n",
       "9                    NaN         0      0                   []          True  \n",
       "10                   NaN         0      0                   []         False  \n",
       "11                   NaN         0      0                   []         False  \n",
       "12                   NaN         0      0                   []          True  \n",
       "13                   NaN         0      0                   []         False  \n",
       "14                   NaN         0      0                   []          True  \n",
       "15                   NaN         0      0                   []          True  \n",
       "16                   NaN         0      0                   []          True  \n",
       "17                   NaN         0      0                   []         False  \n",
       "18                   NaN         0      0                   []         False  \n",
       "19                   NaN         0      0                   []          True  \n",
       "\n",
       "[20 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  4 读取 Excel 文件｜指定行（跳过）\n",
    "<br>\n",
    "\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件并<font color = '#5F5FFC'>跳过前20行</font>"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "data = pd.read_csv(\"./某招聘网站数据.csv\", header=0, skiprows=lambda x: x > 0 and x < 20 )\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionId</th>\n",
       "      <th>positionName</th>\n",
       "      <th>companyId</th>\n",
       "      <th>companySize</th>\n",
       "      <th>industryField</th>\n",
       "      <th>financeStage</th>\n",
       "      <th>companyLabelList</th>\n",
       "      <th>firstType</th>\n",
       "      <th>secondType</th>\n",
       "      <th>thirdType</th>\n",
       "      <th>...</th>\n",
       "      <th>plus</th>\n",
       "      <th>pcShow</th>\n",
       "      <th>appShow</th>\n",
       "      <th>deliver</th>\n",
       "      <th>gradeDescription</th>\n",
       "      <th>promotionScoreExplain</th>\n",
       "      <th>isHotHire</th>\n",
       "      <th>count</th>\n",
       "      <th>aggregatePositionIds</th>\n",
       "      <th>famousCompany</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6702852</td>\n",
       "      <td>数据分析专家（游戏业务）</td>\n",
       "      <td>593</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,游戏</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['五险一金', '交通补助', '绩效奖金', '节日礼物']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6829277</td>\n",
       "      <td>资深数据分析师</td>\n",
       "      <td>593</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,游戏</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['五险一金', '交通补助', '绩效奖金', '节日礼物']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>高端产品职位</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6267370</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>31544</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['专业红娘牵线', '节日礼物', '技能培训', '岗位晋升']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5927901</td>\n",
       "      <td>数据分析经理</td>\n",
       "      <td>62</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>文娱丨内容</td>\n",
       "      <td>C轮</td>\n",
       "      <td>['扁平管理', '弹性工作', '大厨定制三餐', '就近租房补贴']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>产品经理</td>\n",
       "      <td>其他产品经理</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6862245</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>473950</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>未融资</td>\n",
       "      <td>[]</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>6884346</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>21236</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>移动互联网,医疗丨健康</td>\n",
       "      <td>C轮</td>\n",
       "      <td>['技能培训', '年底双薪', '节日礼物', '绩效奖金']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>6849100</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>72076</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>C轮</td>\n",
       "      <td>['节日礼物', '股票期权', '带薪年假', '年度旅游']</td>\n",
       "      <td>市场|商务类</td>\n",
       "      <td>市场|营销</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6803432</td>\n",
       "      <td>奔驰·耀出行-BI数据分析专家</td>\n",
       "      <td>751158</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>[]</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>6704835</td>\n",
       "      <td>BI数据分析师</td>\n",
       "      <td>52840</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>电商</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训', '年底双薪', '节日礼物', '绩效奖金']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6728058</td>\n",
       "      <td>数据分析专家-LQ(J181203029)</td>\n",
       "      <td>2474</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>汽车丨出行</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['弹性工作', '节日礼物', '岗位晋升', '技能培训']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>其他数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    positionId           positionName  companyId companySize industryField  \\\n",
       "0      6702852           数据分析专家（游戏业务）        593     2000人以上      移动互联网,游戏   \n",
       "1      6829277                资深数据分析师        593     2000人以上      移动互联网,游戏   \n",
       "2      6267370                 数据分析专家      31544    150-500人          数据服务   \n",
       "3      5927901                 数据分析经理         62     2000人以上         文娱丨内容   \n",
       "4      6862245                 数据分析专家     473950     50-150人         移动互联网   \n",
       "..         ...                    ...        ...         ...           ...   \n",
       "81     6884346                  数据分析师      21236   500-2000人   移动互联网,医疗丨健康   \n",
       "82     6849100                 商业数据分析      72076   500-2000人      移动互联网,电商   \n",
       "83     6803432        奔驰·耀出行-BI数据分析专家     751158    150-500人         移动互联网   \n",
       "84     6704835                BI数据分析师      52840     2000人以上            电商   \n",
       "85     6728058  数据分析专家-LQ(J181203029)       2474     2000人以上         汽车丨出行   \n",
       "\n",
       "   financeStage                      companyLabelList  firstType secondType  \\\n",
       "0         不需要融资      ['五险一金', '交通补助', '绩效奖金', '节日礼物']  开发|测试|运维类       数据开发   \n",
       "1         不需要融资      ['五险一金', '交通补助', '绩效奖金', '节日礼物']  产品|需求|项目类     高端产品职位   \n",
       "2         不需要融资    ['专业红娘牵线', '节日礼物', '技能培训', '岗位晋升']  开发|测试|运维类       数据开发   \n",
       "3            C轮  ['扁平管理', '弹性工作', '大厨定制三餐', '就近租房补贴']  产品|需求|项目类       产品经理   \n",
       "4           未融资                                    []  产品|需求|项目类       数据分析   \n",
       "..          ...                                   ...        ...        ...   \n",
       "81           C轮      ['技能培训', '年底双薪', '节日礼物', '绩效奖金']  产品|需求|项目类       数据分析   \n",
       "82           C轮      ['节日礼物', '股票期权', '带薪年假', '年度旅游']     市场|商务类      市场|营销   \n",
       "83        不需要融资                                    []  开发|测试|运维类       数据开发   \n",
       "84         上市公司      ['技能培训', '年底双薪', '节日礼物', '绩效奖金']  开发|测试|运维类       数据开发   \n",
       "85        不需要融资      ['弹性工作', '节日礼物', '岗位晋升', '技能培训']  产品|需求|项目类       数据分析   \n",
       "\n",
       "   thirdType  ... plus pcShow appShow deliver gradeDescription  \\\n",
       "0       数据分析  ...  NaN      0       0       0              NaN   \n",
       "1     数据分析专家  ...  NaN      0       0       0              NaN   \n",
       "2       数据分析  ...  NaN      0       0       0              NaN   \n",
       "3     其他产品经理  ...  NaN      0       0       0              NaN   \n",
       "4       数据分析  ...  NaN      0       0       0              NaN   \n",
       "..       ...  ...  ...    ...     ...     ...              ...   \n",
       "81      数据分析  ...  NaN      0       0       0              NaN   \n",
       "82    商业数据分析  ...  NaN      0       0       0              NaN   \n",
       "83      数据分析  ...  NaN      0       0       0              NaN   \n",
       "84      数据分析  ...  NaN      0       0       0              NaN   \n",
       "85    其他数据分析  ...  NaN      0       0       0              NaN   \n",
       "\n",
       "   promotionScoreExplain isHotHire  count aggregatePositionIds famousCompany  \n",
       "0                    NaN         0      0                   []          True  \n",
       "1                    NaN         0      0                   []          True  \n",
       "2                    NaN         0      0                   []         False  \n",
       "3                    NaN         0      0                   []          True  \n",
       "4                    NaN         0      0                   []         False  \n",
       "..                   ...       ...    ...                  ...           ...  \n",
       "81                   NaN         0      0                   []         False  \n",
       "82                   NaN         0      0                   []         False  \n",
       "83                   NaN         0      0                   []         False  \n",
       "84                   NaN         0      0                   []          True  \n",
       "85                   NaN         0      0                   []          True  \n",
       "\n",
       "[86 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  5 读取 Excel 文件｜指定行（条件）\n",
    "<br>\n",
    "\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件中全部<font color = '#5F5FFC'>偶数行</font>\n",
    "\n",
    "思考：如果是读取全部奇数行，或者更多满足指定条件的行呢？"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "data = pd.read_csv(\"./某招聘网站数据.csv\", header=0, skiprows=lambda x: x > 0 and x % 2 == 0)\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "      <td>['技能培训', '免费班车', '专项奖金', '岗位晋升']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>6841659</td>\n",
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       "      <td>50-150人</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['大牛团队', '扁平管理', '年底双薪', '股票期权']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
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       "      <td>数据分析</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>6458372</td>\n",
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       "      <td>150-500人</td>\n",
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       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
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       "      <td>34132</td>\n",
       "      <td>150-500人</td>\n",
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       "      <td>A轮</td>\n",
       "      <td>['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
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       "      <td>False</td>\n",
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       "      <td>6763962</td>\n",
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       "      <td>移动互联网</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['绩效奖金', '股票期权', '年底双薪', '专项奖金']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
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       "      <td>数据分析</td>\n",
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       "      <td>企业服务</td>\n",
       "      <td>上市公司</td>\n",
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       "      <td>产品|需求|项目类</td>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6486988</td>\n",
       "      <td>资深数据分析师（杭州）</td>\n",
       "      <td>7461</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['工程师氛围', '弹性工作', '扁平管理', '上班不打卡']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5559894</td>\n",
       "      <td>数据建模专家-杭州-01546</td>\n",
       "      <td>6502</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>信息安全,数据服务</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['技能培训', '股票期权', '绩效奖金', '岗位晋升']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>建模</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6829277</td>\n",
       "      <td>资深数据分析师</td>\n",
       "      <td>593</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,游戏</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['五险一金', '交通补助', '绩效奖金', '节日礼物']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>高端产品职位</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5927901</td>\n",
       "      <td>数据分析经理</td>\n",
       "      <td>62</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>文娱丨内容</td>\n",
       "      <td>C轮</td>\n",
       "      <td>['扁平管理', '弹性工作', '大厨定制三餐', '就近租房补贴']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>产品经理</td>\n",
       "      <td>其他产品经理</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5604926</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>143884</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网,金融</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['股票期权', '带薪年假', '绩效奖金', '年底双薪']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6850849</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>255742</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>金融,电商</td>\n",
       "      <td>C轮</td>\n",
       "      <td>['持牌金融机构', '跨境支付', '跨境金融', '国际化团队']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6657704</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>165220</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>社交</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['绩效奖金', '带薪年假', '交通补助', '午餐补助']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6234992</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>542</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>消费生活</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['六险一金', '快乐高效文化', '绩效奖金', '信任']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6804489</td>\n",
       "      <td>资深数据分析师</td>\n",
       "      <td>34132</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>数据服务,广告营销</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6228290</td>\n",
       "      <td>商业数据分析师</td>\n",
       "      <td>509360</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['定期体检', '帅哥多', '领导好', '美女多']</td>\n",
       "      <td>市场|商务类</td>\n",
       "      <td>市场|营销</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6886661</td>\n",
       "      <td>浙江数据分析师</td>\n",
       "      <td>321001</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>金融,数据服务</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['年底双薪', '午餐补助', '年终分红', '绩效奖金']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6601086</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>21187</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>企业服务,移动互联网</td>\n",
       "      <td>天使轮</td>\n",
       "      <td>['带薪年假', '年轻团队', '股票期权', '下午茶']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6791055</td>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>432882</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['技术大牛', '领导nice', '帅哥美女', '环境超棒']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6344146</td>\n",
       "      <td>资深数据分析师</td>\n",
       "      <td>522865</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>游戏</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['年底双薪', '专项奖金', '提供三餐', '便捷班车']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6653757</td>\n",
       "      <td>银行数据分析岗</td>\n",
       "      <td>23403</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['五险一金', '通讯津贴', '带薪年假', '定期体检']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6228290</td>\n",
       "      <td>商业数据分析师</td>\n",
       "      <td>509360</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['定期体检', '帅哥多', '领导好', '美女多']</td>\n",
       "      <td>市场|商务类</td>\n",
       "      <td>市场|营销</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6067812</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>98316</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>电商,消费生活</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['午餐补助', '带薪年假', '定期体检', '年度旅游']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6680900</td>\n",
       "      <td>数据分析师 (MJ000250)</td>\n",
       "      <td>114335</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['股票期权', '弹性工作', '领导好', '五险一金']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>产品经理</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6486069</td>\n",
       "      <td>解决方案顾问/数据分析师</td>\n",
       "      <td>166666</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>企业服务,数据服务</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['股票期权', '绩效奖金', '带薪年假', '弹性工作']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6046775</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>133429</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网,消费生活</td>\n",
       "      <td>未融资</td>\n",
       "      <td>['年底双薪', '专项奖金', '美女多', '弹性工作']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>高端产品职位</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6037742</td>\n",
       "      <td>资深数据分析师 (MJ000088)</td>\n",
       "      <td>127233</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['带薪年假', '绩效奖金', '定期体检', '弹性工作']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5921220</td>\n",
       "      <td>财务数据分析师</td>\n",
       "      <td>137388</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['年终分红', '绩效奖金', '定期体检', '年底双薪']</td>\n",
       "      <td>综合职能|高级管理</td>\n",
       "      <td>财务</td>\n",
       "      <td>财务风控</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5978750</td>\n",
       "      <td>数据分析师（保险）13-01-19</td>\n",
       "      <td>18655</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>汽车丨出行</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['技能培训', 'Geek', '开放', '扁平管理']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>6191993</td>\n",
       "      <td>数据分析专家03-10-217</td>\n",
       "      <td>18655</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>汽车丨出行</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['技能培训', 'Geek', '开放', '扁平管理']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>6789831</td>\n",
       "      <td>数据分析实习生</td>\n",
       "      <td>306282</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>移动互联网,数据服务</td>\n",
       "      <td>未融资</td>\n",
       "      <td>['弹性工作', '技能培训', '岗位晋升', '五险一金']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6848382</td>\n",
       "      <td>资深数据分析师（商品方向）G01053</td>\n",
       "      <td>179243</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>电商,移动互联网</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['绩效奖金', '股票期权', '五险一金', '午餐补助']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>6829736</td>\n",
       "      <td>数据分析负责人 or 数据分析师</td>\n",
       "      <td>205347</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>金融,电商</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['带薪年假', '定期体检', '免费班车', '领导好']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>6728610</td>\n",
       "      <td>高级数据分析专员</td>\n",
       "      <td>98316</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>电商,消费生活</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['午餐补助', '带薪年假', '定期体检', '年度旅游']</td>\n",
       "      <td>市场|商务类</td>\n",
       "      <td>市场|营销</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>6794326</td>\n",
       "      <td>BI数据分析师</td>\n",
       "      <td>374014</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>移动互联网,金融</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['弹性工作', '扁平管理', '领导好', '五险一金']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>BI</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>6837340</td>\n",
       "      <td>数据分析-2020届春招</td>\n",
       "      <td>205347</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>金融,电商</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['带薪年假', '定期体检', '免费班车', '领导好']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>6896403</td>\n",
       "      <td>智能数据分析引擎研发专家</td>\n",
       "      <td>285786</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>电商</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['年底双薪', '带薪年假', '午餐补助', '弹性工作']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>后端开发</td>\n",
       "      <td>Java</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5683671</td>\n",
       "      <td>数据分析实习生 (MJ000087)</td>\n",
       "      <td>114335</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['股票期权', '弹性工作', '领导好', '五险一金']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>后端开发</td>\n",
       "      <td>数据采集</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>6310387</td>\n",
       "      <td>业务与数据分析师</td>\n",
       "      <td>93448</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>人工智能,数据服务</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['技能培训', '股票期权', '带薪年假', '绩效奖金']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>6820395</td>\n",
       "      <td>产品经理/数据分析（核心业务）-2020届春招</td>\n",
       "      <td>205347</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>金融,电商</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['带薪年假', '定期体检', '免费班车', '领导好']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>产品经理</td>\n",
       "      <td>产品经理</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>6747553</td>\n",
       "      <td>资深数据分析师</td>\n",
       "      <td>581460</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>电商,汽车丨出行</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['阿里合资', '六险一金', '独角兽', '行业先驱']</td>\n",
       "      <td>市场|商务类</td>\n",
       "      <td>市场|营销</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>6496980</td>\n",
       "      <td>数据分析师-Lark</td>\n",
       "      <td>62</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>文娱丨内容</td>\n",
       "      <td>C轮</td>\n",
       "      <td>['扁平管理', '弹性工作', '大厨定制三餐', '就近租房补贴']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>其他数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>6597345</td>\n",
       "      <td>资深数据分析师</td>\n",
       "      <td>57350</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['岗位晋升', '年度旅游', '年底双薪', '午餐补助']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6456921</td>\n",
       "      <td>数据分析专家</td>\n",
       "      <td>738016</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>电商,数据服务</td>\n",
       "      <td>未融资</td>\n",
       "      <td>[]</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6813626</td>\n",
       "      <td>资深数据分析专员</td>\n",
       "      <td>165939</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['年底双薪', '带薪年假', '午餐补助', '定期体检']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>6818950</td>\n",
       "      <td>资深数据分析师</td>\n",
       "      <td>165939</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>数据服务</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['年底双薪', '带薪年假', '午餐补助', '定期体检']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>6767669</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>92417</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,广告营销</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['节日礼物', '股票期权', '带薪年假', '岗位晋升']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>6655562</td>\n",
       "      <td>数据分析建模工程师</td>\n",
       "      <td>117422215</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>数据服务,信息安全</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['午餐补助', '带薪年假', '16到18薪', '法定节假日']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>机器学习</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>6884346</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>21236</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>移动互联网,医疗丨健康</td>\n",
       "      <td>C轮</td>\n",
       "      <td>['技能培训', '年底双薪', '节日礼物', '绩效奖金']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6803432</td>\n",
       "      <td>奔驰·耀出行-BI数据分析专家</td>\n",
       "      <td>751158</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>移动互联网</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>[]</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6728058</td>\n",
       "      <td>数据分析专家-LQ(J181203029)</td>\n",
       "      <td>2474</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>汽车丨出行</td>\n",
       "      <td>不需要融资</td>\n",
       "      <td>['弹性工作', '节日礼物', '岗位晋升', '技能培训']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>其他数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    positionId             positionName  companyId companySize industryField  \\\n",
       "0      6802721                     数据分析     475770     50-150人      移动互联网,电商   \n",
       "1      6877668                     数据分析     100125     2000人以上    移动互联网,企业服务   \n",
       "2      6467417                     数据分析      29211     2000人以上         物流丨运输   \n",
       "3      6841659                     数据分析     348784     50-150人      移动互联网,电商   \n",
       "4      6458372                   数据分析专家      34132    150-500人     数据服务,广告营销   \n",
       "5      6804629                    数据分析师      34132    150-500人     数据服务,广告营销   \n",
       "6      6763962                  数据分析工程师      13163   500-2000人         移动互联网   \n",
       "7      6657285                    数据分析师       7461     2000人以上          企业服务   \n",
       "8      6486988              资深数据分析师（杭州）       7461     2000人以上          企业服务   \n",
       "9      5559894          数据建模专家-杭州-01546       6502   500-2000人     信息安全,数据服务   \n",
       "10     6829277                  资深数据分析师        593     2000人以上      移动互联网,游戏   \n",
       "11     5927901                   数据分析经理         62     2000人以上         文娱丨内容   \n",
       "12     5604926                    数据分析师     143884     50-150人      移动互联网,金融   \n",
       "13     6850849                   数据分析专家     255742    150-500人         金融,电商   \n",
       "14     6657704                    数据分析师     165220    150-500人            社交   \n",
       "15     6234992                    数据分析师        542   500-2000人          消费生活   \n",
       "16     6804489                  资深数据分析师      34132    150-500人     数据服务,广告营销   \n",
       "17     6228290                  商业数据分析师     509360     50-150人    移动互联网,企业服务   \n",
       "18     6886661                  浙江数据分析师     321001    150-500人       金融,数据服务   \n",
       "19     6601086                    数据分析师      21187    150-500人    企业服务,移动互联网   \n",
       "20     6791055                  高级数据分析师     432882    150-500人         移动互联网   \n",
       "21     6344146                  资深数据分析师     522865    150-500人            游戏   \n",
       "22     6653757                  银行数据分析岗      23403     2000人以上          企业服务   \n",
       "23     6228290                  商业数据分析师     509360     50-150人    移动互联网,企业服务   \n",
       "24     6067812                   数据分析专员      98316     2000人以上       电商,消费生活   \n",
       "25     6680900         数据分析师 (MJ000250)     114335    150-500人          数据服务   \n",
       "26     6486069             解决方案顾问/数据分析师     166666    150-500人     企业服务,数据服务   \n",
       "27     6046775                   数据分析专家     133429     50-150人    移动互联网,消费生活   \n",
       "28     6037742       资深数据分析师 (MJ000088)     127233    150-500人          电子商务   \n",
       "29     5921220                  财务数据分析师     137388    150-500人      移动互联网,电商   \n",
       "30     5978750        数据分析师（保险）13-01-19      18655     2000人以上         汽车丨出行   \n",
       "31     6191993          数据分析专家03-10-217      18655     2000人以上         汽车丨出行   \n",
       "32     6789831                  数据分析实习生     306282    150-500人    移动互联网,数据服务   \n",
       "33     6848382      资深数据分析师（商品方向）G01053     179243   500-2000人      电商,移动互联网   \n",
       "34     6829736         数据分析负责人 or 数据分析师     205347     2000人以上         金融,电商   \n",
       "35     6728610                 高级数据分析专员      98316     2000人以上       电商,消费生活   \n",
       "36     6794326                  BI数据分析师     374014   500-2000人      移动互联网,金融   \n",
       "37     6837340             数据分析-2020届春招     205347     2000人以上         金融,电商   \n",
       "38     6896403             智能数据分析引擎研发专家     285786     2000人以上            电商   \n",
       "39     5683671       数据分析实习生 (MJ000087)     114335    150-500人          数据服务   \n",
       "40     6310387                 业务与数据分析师      93448    150-500人     人工智能,数据服务   \n",
       "41     6820395  产品经理/数据分析（核心业务）-2020届春招     205347     2000人以上         金融,电商   \n",
       "42     6747553                  资深数据分析师     581460     2000人以上      电商,汽车丨出行   \n",
       "43     6496980               数据分析师-Lark         62     2000人以上         文娱丨内容   \n",
       "44     6597345                  资深数据分析师      57350   500-2000人         移动互联网   \n",
       "45     6456921                   数据分析专家     738016     50-150人       电商,数据服务   \n",
       "46     6813626                 资深数据分析专员     165939    150-500人          数据服务   \n",
       "47     6818950                  资深数据分析师     165939    150-500人          数据服务   \n",
       "48     6767669                   数据分析专员      92417     2000人以上    移动互联网,广告营销   \n",
       "49     6655562                数据分析建模工程师  117422215     50-150人     数据服务,信息安全   \n",
       "50     6884346                    数据分析师      21236   500-2000人   移动互联网,医疗丨健康   \n",
       "51     6803432          奔驰·耀出行-BI数据分析专家     751158    150-500人         移动互联网   \n",
       "52     6728058    数据分析专家-LQ(J181203029)       2474     2000人以上         汽车丨出行   \n",
       "\n",
       "   financeStage                      companyLabelList  firstType secondType  \\\n",
       "0            A轮      ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析   \n",
       "1          上市公司      ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析   \n",
       "2          上市公司      ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类       数据分析   \n",
       "3            A轮      ['大牛团队', '扁平管理', '年底双薪', '股票期权']  产品|需求|项目类       数据分析   \n",
       "4            A轮   ['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']  产品|需求|项目类       数据分析   \n",
       "5            A轮   ['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']  产品|需求|项目类       数据分析   \n",
       "6          上市公司      ['绩效奖金', '股票期权', '年底双薪', '专项奖金']  开发|测试|运维类       数据开发   \n",
       "7          上市公司    ['工程师氛围', '弹性工作', '扁平管理', '上班不打卡']  产品|需求|项目类       数据分析   \n",
       "8          上市公司    ['工程师氛围', '弹性工作', '扁平管理', '上班不打卡']  开发|测试|运维类       数据开发   \n",
       "9         D轮及以上      ['技能培训', '股票期权', '绩效奖金', '岗位晋升']  开发|测试|运维类       数据开发   \n",
       "10        不需要融资      ['五险一金', '交通补助', '绩效奖金', '节日礼物']  产品|需求|项目类     高端产品职位   \n",
       "11           C轮  ['扁平管理', '弹性工作', '大厨定制三餐', '就近租房补贴']  产品|需求|项目类       产品经理   \n",
       "12           A轮      ['股票期权', '带薪年假', '绩效奖金', '年底双薪']  开发|测试|运维类       数据开发   \n",
       "13           C轮   ['持牌金融机构', '跨境支付', '跨境金融', '国际化团队']  开发|测试|运维类       数据开发   \n",
       "14        不需要融资      ['绩效奖金', '带薪年假', '交通补助', '午餐补助']  产品|需求|项目类       数据分析   \n",
       "15        D轮及以上      ['六险一金', '快乐高效文化', '绩效奖金', '信任']  产品|需求|项目类       数据分析   \n",
       "16           A轮   ['开放式办公', '扁平管理', '带薪假期', '弹性工作时间']  开发|测试|运维类       数据开发   \n",
       "17           B轮         ['定期体检', '帅哥多', '领导好', '美女多']     市场|商务类      市场|营销   \n",
       "18        D轮及以上      ['年底双薪', '午餐补助', '年终分红', '绩效奖金']  开发|测试|运维类       数据开发   \n",
       "19          天使轮       ['带薪年假', '年轻团队', '股票期权', '下午茶']  产品|需求|项目类       数据分析   \n",
       "20        不需要融资    ['技术大牛', '领导nice', '帅哥美女', '环境超棒']  产品|需求|项目类       数据分析   \n",
       "21        不需要融资      ['年底双薪', '专项奖金', '提供三餐', '便捷班车']  开发|测试|运维类       数据开发   \n",
       "22         上市公司      ['五险一金', '通讯津贴', '带薪年假', '定期体检']  开发|测试|运维类       数据开发   \n",
       "23           B轮         ['定期体检', '帅哥多', '领导好', '美女多']     市场|商务类      市场|营销   \n",
       "24         上市公司      ['午餐补助', '带薪年假', '定期体检', '年度旅游']  产品|需求|项目类       数据分析   \n",
       "25           B轮       ['股票期权', '弹性工作', '领导好', '五险一金']  产品|需求|项目类       产品经理   \n",
       "26           B轮      ['股票期权', '绩效奖金', '带薪年假', '弹性工作']  产品|需求|项目类       数据分析   \n",
       "27          未融资       ['年底双薪', '专项奖金', '美女多', '弹性工作']  产品|需求|项目类     高端产品职位   \n",
       "28        不需要融资      ['带薪年假', '绩效奖金', '定期体检', '弹性工作']  开发|测试|运维类       数据开发   \n",
       "29        不需要融资      ['年终分红', '绩效奖金', '定期体检', '年底双薪']  综合职能|高级管理         财务   \n",
       "30        D轮及以上        ['技能培训', 'Geek', '开放', '扁平管理']  开发|测试|运维类       数据开发   \n",
       "31        D轮及以上        ['技能培训', 'Geek', '开放', '扁平管理']  开发|测试|运维类       数据开发   \n",
       "32          未融资      ['弹性工作', '技能培训', '岗位晋升', '五险一金']  开发|测试|运维类       数据开发   \n",
       "33         上市公司      ['绩效奖金', '股票期权', '五险一金', '午餐补助']  产品|需求|项目类       数据分析   \n",
       "34         上市公司       ['带薪年假', '定期体检', '免费班车', '领导好']  开发|测试|运维类       数据开发   \n",
       "35         上市公司      ['午餐补助', '带薪年假', '定期体检', '年度旅游']     市场|商务类      市场|营销   \n",
       "36           B轮       ['弹性工作', '扁平管理', '领导好', '五险一金']  产品|需求|项目类       数据分析   \n",
       "37         上市公司       ['带薪年假', '定期体检', '免费班车', '领导好']  产品|需求|项目类       数据分析   \n",
       "38         上市公司      ['年底双薪', '带薪年假', '午餐补助', '弹性工作']  开发|测试|运维类       后端开发   \n",
       "39           B轮       ['股票期权', '弹性工作', '领导好', '五险一金']  开发|测试|运维类       后端开发   \n",
       "40           B轮      ['技能培训', '股票期权', '带薪年假', '绩效奖金']  产品|需求|项目类       数据分析   \n",
       "41         上市公司       ['带薪年假', '定期体检', '免费班车', '领导好']  产品|需求|项目类       产品经理   \n",
       "42        D轮及以上       ['阿里合资', '六险一金', '独角兽', '行业先驱']     市场|商务类      市场|营销   \n",
       "43           C轮  ['扁平管理', '弹性工作', '大厨定制三餐', '就近租房补贴']  产品|需求|项目类       数据分析   \n",
       "44           A轮      ['岗位晋升', '年度旅游', '年底双薪', '午餐补助']  产品|需求|项目类       数据分析   \n",
       "45          未融资                                    []  开发|测试|运维类       数据开发   \n",
       "46        不需要融资      ['年底双薪', '带薪年假', '午餐补助', '定期体检']  开发|测试|运维类       数据开发   \n",
       "47        不需要融资      ['年底双薪', '带薪年假', '午餐补助', '定期体检']  开发|测试|运维类       数据开发   \n",
       "48         上市公司      ['节日礼物', '股票期权', '带薪年假', '岗位晋升']  产品|需求|项目类       数据分析   \n",
       "49           A轮   ['午餐补助', '带薪年假', '16到18薪', '法定节假日']  开发|测试|运维类       人工智能   \n",
       "50           C轮      ['技能培训', '年底双薪', '节日礼物', '绩效奖金']  产品|需求|项目类       数据分析   \n",
       "51        不需要融资                                    []  开发|测试|运维类       数据开发   \n",
       "52        不需要融资      ['弹性工作', '节日礼物', '岗位晋升', '技能培训']  产品|需求|项目类       数据分析   \n",
       "\n",
       "   thirdType  ... plus pcShow appShow deliver gradeDescription  \\\n",
       "0       数据分析  ...  NaN      0       0       0              NaN   \n",
       "1       数据分析  ...  NaN      0       0       0              NaN   \n",
       "2       数据分析  ...  NaN      0       0       0              NaN   \n",
       "3       数据分析  ...  NaN      0       0       0              NaN   \n",
       "4     其他数据分析  ...  NaN      0       0       0              NaN   \n",
       "5       数据分析  ...  NaN      0       0       0              NaN   \n",
       "6       数据分析  ...  NaN      0       0       0              NaN   \n",
       "7       数据分析  ...  NaN      0       0       0              NaN   \n",
       "8       数据分析  ...  NaN      0       0       0              NaN   \n",
       "9         建模  ...  NaN      0       0       0              NaN   \n",
       "10    数据分析专家  ...  NaN      0       0       0              NaN   \n",
       "11    其他产品经理  ...  NaN      0       0       0              NaN   \n",
       "12      数据分析  ...  NaN      0       0       0              NaN   \n",
       "13      数据分析  ...  NaN      0       0       0              NaN   \n",
       "14      数据分析  ...  NaN      0       0       0              NaN   \n",
       "15      数据分析  ...  NaN      0       0       0              NaN   \n",
       "16      数据分析  ...  NaN      0       0       0              NaN   \n",
       "17    商业数据分析  ...  NaN      0       0       0              NaN   \n",
       "18      数据分析  ...  NaN      0       0       0              NaN   \n",
       "19      数据分析  ...  NaN      0       0       0              NaN   \n",
       "20      数据分析  ...  NaN      0       0       0              NaN   \n",
       "21      数据分析  ...  NaN      0       0       0              NaN   \n",
       "22      数据分析  ...  NaN      0       0       0              NaN   \n",
       "23    商业数据分析  ...  NaN      0       0       0              NaN   \n",
       "24      数据分析  ...  NaN      0       0       0              NaN   \n",
       "25     数据分析师  ...  NaN      0       0       0              NaN   \n",
       "26      数据分析  ...  NaN      0       0       0              NaN   \n",
       "27    数据分析专家  ...  NaN      0       0       0              NaN   \n",
       "28      数据分析  ...  NaN      0       0       0              NaN   \n",
       "29      财务风控  ...  NaN      0       0       0              NaN   \n",
       "30      数据分析  ...  NaN      0       0       0              NaN   \n",
       "31      数据分析  ...  NaN      0       0       0              NaN   \n",
       "32      数据分析  ...  NaN      0       0       0              NaN   \n",
       "33      数据分析  ...  NaN      0       0       0              NaN   \n",
       "34      数据分析  ...  NaN      0       0       0              NaN   \n",
       "35    商业数据分析  ...  NaN      0       0       0              NaN   \n",
       "36        BI  ...  NaN      0       0       0              NaN   \n",
       "37      数据分析  ...  NaN      0       0       0              NaN   \n",
       "38      Java  ...  NaN      0       0       0              NaN   \n",
       "39      数据采集  ...  NaN      0       0       0              NaN   \n",
       "40      数据分析  ...  NaN      0       0       0              NaN   \n",
       "41      产品经理  ...  NaN      0       0       0              NaN   \n",
       "42    商业数据分析  ...  NaN      0       0       0              NaN   \n",
       "43    其他数据分析  ...  NaN      0       0       0              NaN   \n",
       "44      数据分析  ...  NaN      0       0       0              NaN   \n",
       "45      数据分析  ...  NaN      0       0       0              NaN   \n",
       "46      数据分析  ...  NaN      0       0       0              NaN   \n",
       "47      数据分析  ...  NaN      0       0       0              NaN   \n",
       "48      数据分析  ...  NaN      0       0       0              NaN   \n",
       "49      机器学习  ...  NaN      0       0       0              NaN   \n",
       "50      数据分析  ...  NaN      0       0       0              NaN   \n",
       "51      数据分析  ...  NaN      0       0       0              NaN   \n",
       "52    其他数据分析  ...  NaN      0       0       0              NaN   \n",
       "\n",
       "   promotionScoreExplain isHotHire  count aggregatePositionIds famousCompany  \n",
       "0                    NaN         0      0                   []         False  \n",
       "1                    NaN         0      0                   []         False  \n",
       "2                    NaN         0      0                   []          True  \n",
       "3                    NaN         0      0                   []         False  \n",
       "4                    NaN         0      0                   []         False  \n",
       "5                    NaN         0      0                   []         False  \n",
       "6                    NaN         0      0                   []          True  \n",
       "7                    NaN         0      0                   []          True  \n",
       "8                    NaN         0      0                   []          True  \n",
       "9                    NaN         0      0                   []         False  \n",
       "10                   NaN         0      0                   []          True  \n",
       "11                   NaN         0      0                   []          True  \n",
       "12                   NaN         0      0                   []         False  \n",
       "13                   NaN         0      0                   []         False  \n",
       "14                   NaN         0      0                   []         False  \n",
       "15                   NaN         0      0                   []          True  \n",
       "16                   NaN         0      0                   []         False  \n",
       "17                   NaN         0      0                   []         False  \n",
       "18                   NaN         0      0                   []         False  \n",
       "19                   NaN         0      0                   []         False  \n",
       "20                   NaN         0      0                   []         False  \n",
       "21                   NaN         0      0                   []         False  \n",
       "22                   NaN         0      0                   []         False  \n",
       "23                   NaN         0      0                   []         False  \n",
       "24                   NaN         0      0                   []         False  \n",
       "25                   NaN         0      0                   []         False  \n",
       "26                   NaN         0      0                   []         False  \n",
       "27                   NaN         0      0                   []         False  \n",
       "28                   NaN         0      0                   []         False  \n",
       "29                   NaN         0      0                   []         False  \n",
       "30                   NaN         0      0                   []          True  \n",
       "31                   NaN         0      0                   []          True  \n",
       "32                   NaN         0      0                   []         False  \n",
       "33                   NaN         0      0                   []          True  \n",
       "34                   NaN         0      0                   []          True  \n",
       "35                   NaN         0      0                   []         False  \n",
       "36                   NaN         0      0                   []         False  \n",
       "37                   NaN         0      0                   []          True  \n",
       "38                   NaN         0      0                   []          True  \n",
       "39                   NaN         0      0                   []         False  \n",
       "40                   NaN         0      0                   []         False  \n",
       "41                   NaN         0      0                   []          True  \n",
       "42                   NaN         0      0                   []         False  \n",
       "43                   NaN         0      0                   []          True  \n",
       "44                   NaN         0      0                   []         False  \n",
       "45                   NaN         0      0                   []         False  \n",
       "46                   NaN         0      0                   []         False  \n",
       "47                   NaN         0      0                   []         False  \n",
       "48                   NaN         0      0                   []         False  \n",
       "49                   NaN         0      0                   []         False  \n",
       "50                   NaN         0      0                   []         False  \n",
       "51                   NaN         0      0                   []         False  \n",
       "52                   NaN         0      0                   []          True  \n",
       "\n",
       "[53 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 6 读取 Excel 文件｜指定列（列号）\n",
    "\n",
    "<br>\n",
    "\n",
    "**根据指定列号读取**\n",
    "\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件的第 `1、3、5` 列"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "data = pd.read_csv(\"./某招聘网站数据.csv\", header=0, skiprows=lambda x: x > 0 and not(x in [1,3,5]) )\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionId</th>\n",
       "      <th>positionName</th>\n",
       "      <th>companyId</th>\n",
       "      <th>companySize</th>\n",
       "      <th>industryField</th>\n",
       "      <th>financeStage</th>\n",
       "      <th>companyLabelList</th>\n",
       "      <th>firstType</th>\n",
       "      <th>secondType</th>\n",
       "      <th>thirdType</th>\n",
       "      <th>...</th>\n",
       "      <th>plus</th>\n",
       "      <th>pcShow</th>\n",
       "      <th>appShow</th>\n",
       "      <th>deliver</th>\n",
       "      <th>gradeDescription</th>\n",
       "      <th>promotionScoreExplain</th>\n",
       "      <th>isHotHire</th>\n",
       "      <th>count</th>\n",
       "      <th>aggregatePositionIds</th>\n",
       "      <th>famousCompany</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6802721</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>475770</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['绩效奖金', '带薪年假', '定期体检', '弹性工作']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6877668</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>100125</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['节日礼物', '年底双薪', '股票期权', '带薪年假']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6467417</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>29211</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>物流丨运输</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训', '免费班车', '专项奖金', '岗位晋升']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   positionId positionName  companyId companySize industryField financeStage  \\\n",
       "0     6802721         数据分析     475770     50-150人      移动互联网,电商           A轮   \n",
       "1     6877668         数据分析     100125     2000人以上    移动互联网,企业服务         上市公司   \n",
       "2     6467417         数据分析      29211     2000人以上         物流丨运输         上市公司   \n",
       "\n",
       "                   companyLabelList  firstType secondType thirdType  ... plus  \\\n",
       "0  ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析      数据分析  ...  NaN   \n",
       "1  ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析      数据分析  ...  NaN   \n",
       "2  ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类       数据分析      数据分析  ...  NaN   \n",
       "\n",
       "  pcShow appShow deliver gradeDescription promotionScoreExplain isHotHire  \\\n",
       "0      0       0       0              NaN                   NaN         0   \n",
       "1      0       0       0              NaN                   NaN         0   \n",
       "2      0       0       0              NaN                   NaN         0   \n",
       "\n",
       "   count aggregatePositionIds famousCompany  \n",
       "0      0                   []         False  \n",
       "1      0                   []         False  \n",
       "2      0                   []          True  \n",
       "\n",
       "[3 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  7 读取 Excel 文件｜指定列（列名）\n",
    "\n",
    "<br>\n",
    "\n",
    "**根据指定列名读取**\n",
    "\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件的 `positionId、positionName、salary` 列"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "data = pd.read_csv(\"./某招聘网站数据.csv\",header=0, usecols=['positionId', 'positionName', 'salary'] )\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionId</th>\n",
       "      <th>positionName</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6802721</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5204912</td>\n",
       "      <td>数据建模</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6877668</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6496141</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6467417</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6884346</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6849100</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>6803432</td>\n",
       "      <td>奔驰·耀出行-BI数据分析专家</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6704835</td>\n",
       "      <td>BI数据分析师</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6728058</td>\n",
       "      <td>数据分析专家-LQ(J181203029)</td>\n",
       "      <td>21500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     positionId           positionName  salary\n",
       "0       6802721                   数据分析   37500\n",
       "1       5204912                   数据建模   15000\n",
       "2       6877668                   数据分析    3500\n",
       "3       6496141                   数据分析   45000\n",
       "4       6467417                   数据分析   30000\n",
       "..          ...                    ...     ...\n",
       "100     6884346                  数据分析师   25000\n",
       "101     6849100                 商业数据分析   35000\n",
       "102     6803432        奔驰·耀出行-BI数据分析专家   30000\n",
       "103     6704835                BI数据分析师   20000\n",
       "104     6728058  数据分析专家-LQ(J181203029)   21500\n",
       "\n",
       "[105 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  8 读取 Excel 文件｜指定列（匹配）\r\n",
    "\r\n",
    "<br>\r\n",
    "\r\n",
    "**根据指定列名匹配读取**\r\n",
    "\r\n",
    "让我们来个更难一点的，还是读取 `某招聘网站数据.csv` 文件，但现在有一个 list 中包含多个字段👇\r\n",
    "\r\n",
    "`usecols = ['positionId','test','positionName', 'test1','salary']`\r\n",
    "\r\n",
    "如果 `usecols` 中的列名存在于 `某招聘网站数据.csv` 中，则读取。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "usecols = ['positionId','test','positionName', 'test1','salary']\r\n",
    "data = pd.read_csv(\"./某招聘网站数据.csv\",header=0, usecols=lambda x: x in usecols )\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionId</th>\n",
       "      <th>positionName</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6802721</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5204912</td>\n",
       "      <td>数据建模</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6877668</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6496141</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6467417</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6884346</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6849100</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>6803432</td>\n",
       "      <td>奔驰·耀出行-BI数据分析专家</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6704835</td>\n",
       "      <td>BI数据分析师</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6728058</td>\n",
       "      <td>数据分析专家-LQ(J181203029)</td>\n",
       "      <td>21500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     positionId           positionName  salary\n",
       "0       6802721                   数据分析   37500\n",
       "1       5204912                   数据建模   15000\n",
       "2       6877668                   数据分析    3500\n",
       "3       6496141                   数据分析   45000\n",
       "4       6467417                   数据分析   30000\n",
       "..          ...                    ...     ...\n",
       "100     6884346                  数据分析师   25000\n",
       "101     6849100                 商业数据分析   35000\n",
       "102     6803432        奔驰·耀出行-BI数据分析专家   30000\n",
       "103     6704835                BI数据分析师   20000\n",
       "104     6728058  数据分析专家-LQ(J181203029)   21500\n",
       "\n",
       "[105 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 9 读取 Excel 文件｜指定索引\n",
    "\n",
    "<br>\n",
    "\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件，并在读取时将 `positionId` 设置为索引列"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "data = pd.read_csv(\"./某招聘网站数据.csv\",header=0, index_col='positionId' )\r\n",
    "data[:5]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionName</th>\n",
       "      <th>companyId</th>\n",
       "      <th>companySize</th>\n",
       "      <th>industryField</th>\n",
       "      <th>financeStage</th>\n",
       "      <th>companyLabelList</th>\n",
       "      <th>firstType</th>\n",
       "      <th>secondType</th>\n",
       "      <th>thirdType</th>\n",
       "      <th>skillLables</th>\n",
       "      <th>...</th>\n",
       "      <th>plus</th>\n",
       "      <th>pcShow</th>\n",
       "      <th>appShow</th>\n",
       "      <th>deliver</th>\n",
       "      <th>gradeDescription</th>\n",
       "      <th>promotionScoreExplain</th>\n",
       "      <th>isHotHire</th>\n",
       "      <th>count</th>\n",
       "      <th>aggregatePositionIds</th>\n",
       "      <th>famousCompany</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>positionId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6802721</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>475770</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['绩效奖金', '带薪年假', '定期体检', '弹性工作']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>['SQL', '数据库', '数据运营', 'BI']</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5204912</th>\n",
       "      <td>数据建模</td>\n",
       "      <td>50735</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>电商</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['年终奖金', '做五休二', '六险一金', '子女福利']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>建模</td>\n",
       "      <td>['算法', '数据架构']</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6877668</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>100125</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['节日礼物', '年底双薪', '股票期权', '带薪年假']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>['数据库', '数据分析', 'SQL']</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6496141</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>26564</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>电商</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['生日趴', '每月腐败基金', '每月补贴', '年度旅游']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6467417</th>\n",
       "      <td>数据分析</td>\n",
       "      <td>29211</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>物流丨运输</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训', '免费班车', '专项奖金', '岗位晋升']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>['BI', '数据分析', '数据运营']</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           positionName  companyId companySize industryField financeStage  \\\n",
       "positionId                                                                  \n",
       "6802721            数据分析     475770     50-150人      移动互联网,电商           A轮   \n",
       "5204912            数据建模      50735    150-500人            电商           B轮   \n",
       "6877668            数据分析     100125     2000人以上    移动互联网,企业服务         上市公司   \n",
       "6496141            数据分析      26564   500-2000人            电商        D轮及以上   \n",
       "6467417            数据分析      29211     2000人以上         物流丨运输         上市公司   \n",
       "\n",
       "                             companyLabelList  firstType secondType thirdType  \\\n",
       "positionId                                                                      \n",
       "6802721      ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析      数据分析   \n",
       "5204912      ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类       数据开发        建模   \n",
       "6877668      ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析      数据分析   \n",
       "6496141     ['生日趴', '每月腐败基金', '每月补贴', '年度旅游']  开发|测试|运维类       数据开发      数据分析   \n",
       "6467417      ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类       数据分析      数据分析   \n",
       "\n",
       "                             skillLables  ... plus pcShow appShow deliver  \\\n",
       "positionId                                ...                               \n",
       "6802721     ['SQL', '数据库', '数据运营', 'BI']  ...  NaN      0       0       0   \n",
       "5204912                   ['算法', '数据架构']  ...  NaN      0       0       0   \n",
       "6877668           ['数据库', '数据分析', 'SQL']  ...  NaN      0       0       0   \n",
       "6496141                               []  ...  NaN      0       0       0   \n",
       "6467417           ['BI', '数据分析', '数据运营']  ...  NaN      0       0       0   \n",
       "\n",
       "           gradeDescription promotionScoreExplain  isHotHire count  \\\n",
       "positionId                                                           \n",
       "6802721                 NaN                   NaN          0     0   \n",
       "5204912                 NaN                   NaN          0     0   \n",
       "6877668                 NaN                   NaN          0     0   \n",
       "6496141                 NaN                   NaN          0     0   \n",
       "6467417                 NaN                   NaN          0     0   \n",
       "\n",
       "           aggregatePositionIds famousCompany  \n",
       "positionId                                     \n",
       "6802721                      []         False  \n",
       "5204912                      []         False  \n",
       "6877668                      []         False  \n",
       "6496141                      []          True  \n",
       "6467417                      []          True  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  10 读取 Excel 文件｜指定标题\n",
    "\n",
    "<br>\n",
    "\n",
    "\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件的 `positionId、positionName、salary` 列，并将标题设置为 `ID、岗位名称、薪资`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "source": [
    "head = pd.read_csv(\"./某招聘网站数据.csv\", header=0, nrows=0)\r\n",
    "columns = head.columns.to_list()\r\n",
    "del head\r\n",
    "data = pd.read_csv(\"./某招聘网站数据.csv\", header=0, usecols=[columns.index('positionId'), columns.index('positionName'), columns.index('salary')], names=['ID','岗位名称','薪资'])\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>岗位名称</th>\n",
       "      <th>薪资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6802721</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5204912</td>\n",
       "      <td>数据建模</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6877668</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6496141</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6467417</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6884346</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6849100</td>\n",
       "      <td>商业数据分析</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>6803432</td>\n",
       "      <td>奔驰·耀出行-BI数据分析专家</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6704835</td>\n",
       "      <td>BI数据分析师</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6728058</td>\n",
       "      <td>数据分析专家-LQ(J181203029)</td>\n",
       "      <td>21500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID                   岗位名称     薪资\n",
       "0    6802721                   数据分析  37500\n",
       "1    5204912                   数据建模  15000\n",
       "2    6877668                   数据分析   3500\n",
       "3    6496141                   数据分析  45000\n",
       "4    6467417                   数据分析  30000\n",
       "..       ...                    ...    ...\n",
       "100  6884346                  数据分析师  25000\n",
       "101  6849100                 商业数据分析  35000\n",
       "102  6803432        奔驰·耀出行-BI数据分析专家  30000\n",
       "103  6704835                BI数据分析师  20000\n",
       "104  6728058  数据分析专家-LQ(J181203029)  21500\n",
       "\n",
       "[105 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  11 读取 Excel 文件｜缺失值转换\n",
    "\n",
    "<br>\n",
    "\n",
    "\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件，**并不将缺失值标记为 `NA`**\n",
    "\n",
    "思考：为什么要这样做？"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "data = pd.read_csv(\"./某招聘网站数据.csv\",header=0, keep_default_na=False )\r\n",
    "data[:5]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionId</th>\n",
       "      <th>positionName</th>\n",
       "      <th>companyId</th>\n",
       "      <th>companySize</th>\n",
       "      <th>industryField</th>\n",
       "      <th>financeStage</th>\n",
       "      <th>companyLabelList</th>\n",
       "      <th>firstType</th>\n",
       "      <th>secondType</th>\n",
       "      <th>thirdType</th>\n",
       "      <th>...</th>\n",
       "      <th>plus</th>\n",
       "      <th>pcShow</th>\n",
       "      <th>appShow</th>\n",
       "      <th>deliver</th>\n",
       "      <th>gradeDescription</th>\n",
       "      <th>promotionScoreExplain</th>\n",
       "      <th>isHotHire</th>\n",
       "      <th>count</th>\n",
       "      <th>aggregatePositionIds</th>\n",
       "      <th>famousCompany</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6802721</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>475770</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['绩效奖金', '带薪年假', '定期体检', '弹性工作']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5204912</td>\n",
       "      <td>数据建模</td>\n",
       "      <td>50735</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>电商</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['年终奖金', '做五休二', '六险一金', '子女福利']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>建模</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6877668</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>100125</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['节日礼物', '年底双薪', '股票期权', '带薪年假']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6496141</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>26564</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>电商</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['生日趴', '每月腐败基金', '每月补贴', '年度旅游']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6467417</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>29211</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>物流丨运输</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训', '免费班车', '专项奖金', '岗位晋升']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   positionId positionName  companyId companySize industryField financeStage  \\\n",
       "0     6802721         数据分析     475770     50-150人      移动互联网,电商           A轮   \n",
       "1     5204912         数据建模      50735    150-500人            电商           B轮   \n",
       "2     6877668         数据分析     100125     2000人以上    移动互联网,企业服务         上市公司   \n",
       "3     6496141         数据分析      26564   500-2000人            电商        D轮及以上   \n",
       "4     6467417         数据分析      29211     2000人以上         物流丨运输         上市公司   \n",
       "\n",
       "                    companyLabelList  firstType secondType thirdType  ...  \\\n",
       "0   ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "1   ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类       数据开发        建模  ...   \n",
       "2   ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "3  ['生日趴', '每月腐败基金', '每月补贴', '年度旅游']  开发|测试|运维类       数据开发      数据分析  ...   \n",
       "4   ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "\n",
       "  plus pcShow appShow deliver gradeDescription promotionScoreExplain  \\\n",
       "0           0       0       0                                          \n",
       "1           0       0       0                                          \n",
       "2           0       0       0                                          \n",
       "3           0       0       0                                          \n",
       "4           0       0       0                                          \n",
       "\n",
       "  isHotHire  count aggregatePositionIds famousCompany  \n",
       "0         0      0                   []         False  \n",
       "1         0      0                   []         False  \n",
       "2         0      0                   []         False  \n",
       "3         0      0                   []          True  \n",
       "4         0      0                   []          True  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "###  12 读取 Excel 文件｜缺失值标记\n",
    "\n",
    "<br>\n",
    "\n",
    "\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件，**并将`[]`标记为缺失值**\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "source": [
    "data = pd.read_csv(\"./某招聘网站数据.csv\",header=0, na_values=['[]'] )\r\n",
    "data[:5]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionId</th>\n",
       "      <th>positionName</th>\n",
       "      <th>companyId</th>\n",
       "      <th>companySize</th>\n",
       "      <th>industryField</th>\n",
       "      <th>financeStage</th>\n",
       "      <th>companyLabelList</th>\n",
       "      <th>firstType</th>\n",
       "      <th>secondType</th>\n",
       "      <th>thirdType</th>\n",
       "      <th>...</th>\n",
       "      <th>plus</th>\n",
       "      <th>pcShow</th>\n",
       "      <th>appShow</th>\n",
       "      <th>deliver</th>\n",
       "      <th>gradeDescription</th>\n",
       "      <th>promotionScoreExplain</th>\n",
       "      <th>isHotHire</th>\n",
       "      <th>count</th>\n",
       "      <th>aggregatePositionIds</th>\n",
       "      <th>famousCompany</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6802721</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>475770</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['绩效奖金', '带薪年假', '定期体检', '弹性工作']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5204912</td>\n",
       "      <td>数据建模</td>\n",
       "      <td>50735</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>电商</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['年终奖金', '做五休二', '六险一金', '子女福利']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>建模</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6877668</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>100125</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['节日礼物', '年底双薪', '股票期权', '带薪年假']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6496141</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>26564</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>电商</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['生日趴', '每月腐败基金', '每月补贴', '年度旅游']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6467417</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>29211</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>物流丨运输</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训', '免费班车', '专项奖金', '岗位晋升']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   positionId positionName  companyId companySize industryField financeStage  \\\n",
       "0     6802721         数据分析     475770     50-150人      移动互联网,电商           A轮   \n",
       "1     5204912         数据建模      50735    150-500人            电商           B轮   \n",
       "2     6877668         数据分析     100125     2000人以上    移动互联网,企业服务         上市公司   \n",
       "3     6496141         数据分析      26564   500-2000人            电商        D轮及以上   \n",
       "4     6467417         数据分析      29211     2000人以上         物流丨运输         上市公司   \n",
       "\n",
       "                    companyLabelList  firstType secondType thirdType  ...  \\\n",
       "0   ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "1   ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类       数据开发        建模  ...   \n",
       "2   ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "3  ['生日趴', '每月腐败基金', '每月补贴', '年度旅游']  开发|测试|运维类       数据开发      数据分析  ...   \n",
       "4   ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "\n",
       "  plus pcShow appShow deliver gradeDescription promotionScoreExplain  \\\n",
       "0  NaN      0       0       0              NaN                   NaN   \n",
       "1  NaN      0       0       0              NaN                   NaN   \n",
       "2  NaN      0       0       0              NaN                   NaN   \n",
       "3  NaN      0       0       0              NaN                   NaN   \n",
       "4  NaN      0       0       0              NaN                   NaN   \n",
       "\n",
       "  isHotHire  count aggregatePositionIds famousCompany  \n",
       "0         0      0                  NaN         False  \n",
       "1         0      0                  NaN         False  \n",
       "2         0      0                  NaN         False  \n",
       "3         0      0                  NaN          True  \n",
       "4         0      0                  NaN          True  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 45
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 13 读取 Excel 文件｜忽略缺失值\n",
    "\n",
    "<br>\n",
    "\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件，**但不处理缺失值**\n",
    "\n",
    "思考：和之前的有什么不同，为什么这么做？"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 14 读取 Excel 文件｜指定格式\n",
    "\n",
    "<br>\n",
    "\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件，并将 `positionId,companyId` 设置为字符串格式"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "source": [
    "#data = pd.read_csv(\"./某招聘网站数据.csv\",header=0 )\r\n",
    "data = pd.read_csv(\"./某招聘网站数据.csv\",header=0, dtype={\"positionId\": str, \"companyId\": str} )\r\n",
    "data[:5]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionId</th>\n",
       "      <th>positionName</th>\n",
       "      <th>companyId</th>\n",
       "      <th>companySize</th>\n",
       "      <th>industryField</th>\n",
       "      <th>financeStage</th>\n",
       "      <th>companyLabelList</th>\n",
       "      <th>firstType</th>\n",
       "      <th>secondType</th>\n",
       "      <th>thirdType</th>\n",
       "      <th>...</th>\n",
       "      <th>plus</th>\n",
       "      <th>pcShow</th>\n",
       "      <th>appShow</th>\n",
       "      <th>deliver</th>\n",
       "      <th>gradeDescription</th>\n",
       "      <th>promotionScoreExplain</th>\n",
       "      <th>isHotHire</th>\n",
       "      <th>count</th>\n",
       "      <th>aggregatePositionIds</th>\n",
       "      <th>famousCompany</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6802721</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>475770</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['绩效奖金', '带薪年假', '定期体检', '弹性工作']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5204912</td>\n",
       "      <td>数据建模</td>\n",
       "      <td>50735</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>电商</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['年终奖金', '做五休二', '六险一金', '子女福利']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>建模</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6877668</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>100125</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['节日礼物', '年底双薪', '股票期权', '带薪年假']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6496141</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>26564</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>电商</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['生日趴', '每月腐败基金', '每月补贴', '年度旅游']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6467417</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>29211</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>物流丨运输</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训', '免费班车', '专项奖金', '岗位晋升']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  positionId positionName companyId companySize industryField financeStage  \\\n",
       "0    6802721         数据分析    475770     50-150人      移动互联网,电商           A轮   \n",
       "1    5204912         数据建模     50735    150-500人            电商           B轮   \n",
       "2    6877668         数据分析    100125     2000人以上    移动互联网,企业服务         上市公司   \n",
       "3    6496141         数据分析     26564   500-2000人            电商        D轮及以上   \n",
       "4    6467417         数据分析     29211     2000人以上         物流丨运输         上市公司   \n",
       "\n",
       "                    companyLabelList  firstType secondType thirdType  ...  \\\n",
       "0   ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "1   ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类       数据开发        建模  ...   \n",
       "2   ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "3  ['生日趴', '每月腐败基金', '每月补贴', '年度旅游']  开发|测试|运维类       数据开发      数据分析  ...   \n",
       "4   ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "\n",
       "  plus pcShow appShow deliver gradeDescription promotionScoreExplain  \\\n",
       "0  NaN      0       0       0              NaN                   NaN   \n",
       "1  NaN      0       0       0              NaN                   NaN   \n",
       "2  NaN      0       0       0              NaN                   NaN   \n",
       "3  NaN      0       0       0              NaN                   NaN   \n",
       "4  NaN      0       0       0              NaN                   NaN   \n",
       "\n",
       "  isHotHire  count aggregatePositionIds famousCompany  \n",
       "0         0      0                   []         False  \n",
       "1         0      0                   []         False  \n",
       "2         0      0                   []         False  \n",
       "3         0      0                   []          True  \n",
       "4         0      0                   []          True  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 51
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 15 读取 Excel 文件｜指定格式（时间）\n",
    "\n",
    "<br>\n",
    "\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件，并将 `createTime` 列设置为字符串格式"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "source": [
    "data = pd.read_csv(\"./某招聘网站数据.csv\",header=0, dtype={\"createTime\": str} )\r\n",
    "data[:5]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positionId</th>\n",
       "      <th>positionName</th>\n",
       "      <th>companyId</th>\n",
       "      <th>companySize</th>\n",
       "      <th>industryField</th>\n",
       "      <th>financeStage</th>\n",
       "      <th>companyLabelList</th>\n",
       "      <th>firstType</th>\n",
       "      <th>secondType</th>\n",
       "      <th>thirdType</th>\n",
       "      <th>...</th>\n",
       "      <th>plus</th>\n",
       "      <th>pcShow</th>\n",
       "      <th>appShow</th>\n",
       "      <th>deliver</th>\n",
       "      <th>gradeDescription</th>\n",
       "      <th>promotionScoreExplain</th>\n",
       "      <th>isHotHire</th>\n",
       "      <th>count</th>\n",
       "      <th>aggregatePositionIds</th>\n",
       "      <th>famousCompany</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6802721</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>475770</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>移动互联网,电商</td>\n",
       "      <td>A轮</td>\n",
       "      <td>['绩效奖金', '带薪年假', '定期体检', '弹性工作']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5204912</td>\n",
       "      <td>数据建模</td>\n",
       "      <td>50735</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>电商</td>\n",
       "      <td>B轮</td>\n",
       "      <td>['年终奖金', '做五休二', '六险一金', '子女福利']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>建模</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6877668</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>100125</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>移动互联网,企业服务</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['节日礼物', '年底双薪', '股票期权', '带薪年假']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6496141</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>26564</td>\n",
       "      <td>500-2000人</td>\n",
       "      <td>电商</td>\n",
       "      <td>D轮及以上</td>\n",
       "      <td>['生日趴', '每月腐败基金', '每月补贴', '年度旅游']</td>\n",
       "      <td>开发|测试|运维类</td>\n",
       "      <td>数据开发</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6467417</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>29211</td>\n",
       "      <td>2000人以上</td>\n",
       "      <td>物流丨运输</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>['技能培训', '免费班车', '专项奖金', '岗位晋升']</td>\n",
       "      <td>产品|需求|项目类</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   positionId positionName  companyId companySize industryField financeStage  \\\n",
       "0     6802721         数据分析     475770     50-150人      移动互联网,电商           A轮   \n",
       "1     5204912         数据建模      50735    150-500人            电商           B轮   \n",
       "2     6877668         数据分析     100125     2000人以上    移动互联网,企业服务         上市公司   \n",
       "3     6496141         数据分析      26564   500-2000人            电商        D轮及以上   \n",
       "4     6467417         数据分析      29211     2000人以上         物流丨运输         上市公司   \n",
       "\n",
       "                    companyLabelList  firstType secondType thirdType  ...  \\\n",
       "0   ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "1   ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类       数据开发        建模  ...   \n",
       "2   ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "3  ['生日趴', '每月腐败基金', '每月补贴', '年度旅游']  开发|测试|运维类       数据开发      数据分析  ...   \n",
       "4   ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类       数据分析      数据分析  ...   \n",
       "\n",
       "  plus pcShow appShow deliver gradeDescription promotionScoreExplain  \\\n",
       "0  NaN      0       0       0              NaN                   NaN   \n",
       "1  NaN      0       0       0              NaN                   NaN   \n",
       "2  NaN      0       0       0              NaN                   NaN   \n",
       "3  NaN      0       0       0              NaN                   NaN   \n",
       "4  NaN      0       0       0              NaN                   NaN   \n",
       "\n",
       "  isHotHire  count aggregatePositionIds famousCompany  \n",
       "0         0      0                   []         False  \n",
       "1         0      0                   []         False  \n",
       "2         0      0                   []         False  \n",
       "3         0      0                   []          True  \n",
       "4         0      0                   []          True  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 60
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 16 读取 Excel 文件｜分块读取\n",
    "\n",
    "\n",
    "<br>\n",
    "\n",
    "读取当前目录下 `某招聘网站数据.csv` 文件，要求返回一个可迭代对象，每次读取 10 行\n",
    "\n",
    "思考：为什么这样做？"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "source": [
    "data = pd.read_csv(\"./某招聘网站数据.csv\",header=0).itertuples()\r\n",
    "n = 0\r\n",
    "for i in data:\r\n",
    "    print(i.positionName)\r\n",
    "    n += 1\r\n",
    "    if n > 10:\r\n",
    "        break"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "数据分析\n",
      "数据建模\n",
      "数据分析\n",
      "数据分析\n",
      "数据分析\n",
      "数据分析\n",
      "数据分析\n",
      "数据建模工程师\n",
      "数据分析专家\n",
      "数据分析师\n",
      "数据分析师\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 17 读取 txt 文件｜常规\r\n",
    "\r\n",
    "<br>\r\n",
    "\r\n",
    "读取当前目录下 `Titanic.txt` 文件。\r\n",
    "\r\n",
    "注意：在接下来的几种格式文件读取中，对于之前重复的参数/功能将不再整理，仅介绍读取功能。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "source": [
    "data = pd.read_csv(\"./Titanic.txt\")\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>version https://git-lfs.github.com/spec/v1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>oid sha256:c5a58dbb3c71f7e6ea1593430e58b31b359...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>size 28627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "          version https://git-lfs.github.com/spec/v1\n",
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     },
     "metadata": {},
     "execution_count": 68
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 18 读取 txt 文件｜含中文\n",
    "\n",
    "<br>\n",
    "\n",
    "读取当前目录下 `TOP250.txt` 文件。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 19 读取 JSON 文件\r\n",
    "\r\n",
    "\r\n",
    "\r\n",
    "<br>\r\n",
    "\r\n",
    "读取当前目录下 `某基金数据.json` 文件。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "source": [
    "data = pd.read_json('./某基金数据.json')\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>净值日期</th>\n",
       "      <th>单位净值</th>\n",
       "      <th>累计净值</th>\n",
       "      <th>日增长率</th>\n",
       "      <th>申购状态</th>\n",
       "      <th>赎回状态</th>\n",
       "      <th>分红送配</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>1.884</td>\n",
       "      <td>1.884</td>\n",
       "      <td>-0.11%</td>\n",
       "      <td>开放申购</td>\n",
       "      <td>开放赎回</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>1.886</td>\n",
       "      <td>1.886</td>\n",
       "      <td>3.34%</td>\n",
       "      <td>开放申购</td>\n",
       "      <td>开放赎回</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>1.825</td>\n",
       "      <td>1.825</td>\n",
       "      <td>-0.16%</td>\n",
       "      <td>开放申购</td>\n",
       "      <td>开放赎回</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>1.828</td>\n",
       "      <td>1.828</td>\n",
       "      <td>1.33%</td>\n",
       "      <td>开放申购</td>\n",
       "      <td>开放赎回</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>1.804</td>\n",
       "      <td>1.804</td>\n",
       "      <td>0.61%</td>\n",
       "      <td>开放申购</td>\n",
       "      <td>开放赎回</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>1.793</td>\n",
       "      <td>1.793</td>\n",
       "      <td>3.11%</td>\n",
       "      <td>开放申购</td>\n",
       "      <td>开放赎回</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>1.739</td>\n",
       "      <td>1.739</td>\n",
       "      <td>1.64%</td>\n",
       "      <td>开放申购</td>\n",
       "      <td>开放赎回</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>1.711</td>\n",
       "      <td>1.711</td>\n",
       "      <td>7.34%</td>\n",
       "      <td>开放申购</td>\n",
       "      <td>开放赎回</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>1.594</td>\n",
       "      <td>1.594</td>\n",
       "      <td>-7.22%</td>\n",
       "      <td>开放申购</td>\n",
       "      <td>开放赎回</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>1.718</td>\n",
       "      <td>1.718</td>\n",
       "      <td>-2.05%</td>\n",
       "      <td>开放申购</td>\n",
       "      <td>开放赎回</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         净值日期   单位净值   累计净值    日增长率  申购状态  赎回状态  分红送配\n",
       "0  2020-02-13  1.884  1.884  -0.11%  开放申购  开放赎回   NaN\n",
       "1  2020-02-12  1.886  1.886   3.34%  开放申购  开放赎回   NaN\n",
       "2  2020-02-11  1.825  1.825  -0.16%  开放申购  开放赎回   NaN\n",
       "3  2020-02-10  1.828  1.828   1.33%  开放申购  开放赎回   NaN\n",
       "4  2020-02-07  1.804  1.804   0.61%  开放申购  开放赎回   NaN\n",
       "5  2020-02-06  1.793  1.793   3.11%  开放申购  开放赎回   NaN\n",
       "6  2020-02-05  1.739  1.739   1.64%  开放申购  开放赎回   NaN\n",
       "7  2020-02-04  1.711  1.711   7.34%  开放申购  开放赎回   NaN\n",
       "8  2020-02-03  1.594  1.594  -7.22%  开放申购  开放赎回   NaN\n",
       "9  2020-01-23  1.718  1.718  -2.05%  开放申购  开放赎回   NaN"
      ]
     },
     "metadata": {},
     "execution_count": 69
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 20 读取 HDF5 文件\n",
    "\n",
    "<br>\n",
    "\n",
    "`HDF5`是一种特殊的文件格式，常见于在大规模存储数据上\n",
    "\n",
    "关于 `pandas` 与 `hdf5` 格式文件的操作较多，下面仅学习如何读取。\n",
    "\n",
    "读取当前目录下`store_tl.h5`文件"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "微信搜索公众号「早起Python」，关注后可以获得更多资源！"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 21 从剪贴板读取数据\n",
    "\n",
    "<br>\n",
    "\n",
    "打开当前目录下 `Titanic.txt` 文件，全选并复制。\n",
    "\n",
    "现在直接从剪贴板读取数据。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 22 从 SQL 读取数据\n",
    "\n",
    "<br>\n",
    "\n",
    "有时我们需要从 `SQL` 中读取数据，如果先将数据导出再`pandas`读取并不是一个合适的选择。\n",
    "\n",
    "在 `pandas` 中支持直接从 `sql` 中查询并读取。\n",
    "\n",
    "为了方便统一操作，请先执行下面的代码创建数据。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "source": [
    "from sqlite3 import connect\r\n",
    "conn = connect(':memory:')\r\n",
    "df = pd.DataFrame(data=[[0, '10/11/12'], [1, '12/11/10']],\r\n",
    "                  columns=['int_column', 'date_column'])\r\n",
    "df.to_sql('test_data', conn)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "下面将 `SQL` 语句 `SELECT int_column, date_column FROM test_data` 转换为 `DataFrame`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "source": [
    "pd.DataFrame(conn.execute('SELECT int_column, date_column FROM test_data'), columns=['int_column', 'date_column'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>int_column</th>\n",
       "      <th>date_column</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10/11/12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12/11/10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   int_column date_column\n",
       "0           0    10/11/12\n",
       "1           1    12/11/10"
      ]
     },
     "metadata": {},
     "execution_count": 75
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 23 从网页读取数据\n",
    "\n",
    "<br>\n",
    "\n",
    "直接从东京奥运会官网读取奖牌榜数据。\n",
    "\n",
    "目标网站地址为 `https://olympics.com/tokyo-2020/olympic-games/zh/results/all-sports/medal-standings.htm`\n",
    "\n",
    "思考：什么类型的在线表格可以直接读取？"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 24 循环读取数据\n",
    "\n",
    "<br>\n",
    "\n",
    "在本小节 `demodata` 文件夹下有多个 `Excel` 文件，要求一次性循环读取全部文件"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "source": [
    "import os\r\n",
    "alldata = pd.DataFrame()\r\n",
    "for fname in os.listdir(path='./demodata'):\r\n",
    "    if '.xlsx' in fname:\r\n",
    "        fname = os.path.join(\"./demodata\", fname)\r\n",
    "        data = pd.read_excel(fname)\r\n",
    "        alldata = pd.concat([alldata, data])\r\n",
    "\r\n",
    "alldata\r\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>日增长率</th>\n",
       "      <th>涨跌</th>\n",
       "      <th>持有金额</th>\n",
       "      <th>剩余本金</th>\n",
       "      <th>操作</th>\n",
       "      <th>总资产</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>2.92</td>\n",
       "      <td>up</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.51</td>\n",
       "      <td>up</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>1.98</td>\n",
       "      <td>up</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>0.17</td>\n",
       "      <td>up</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>down</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2021-05-26</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>down</td>\n",
       "      <td>20372.810590</td>\n",
       "      <td>4700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25072.810590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>2021-05-27</td>\n",
       "      <td>0.66</td>\n",
       "      <td>up</td>\n",
       "      <td>20507.271140</td>\n",
       "      <td>4700</td>\n",
       "      <td>buy</td>\n",
       "      <td>25207.271140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>2021-05-28</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>down</td>\n",
       "      <td>20545.329687</td>\n",
       "      <td>4600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25145.329687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>2021-05-31</td>\n",
       "      <td>0.06</td>\n",
       "      <td>up</td>\n",
       "      <td>20557.656885</td>\n",
       "      <td>4600</td>\n",
       "      <td>buy</td>\n",
       "      <td>25157.656885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>0.42</td>\n",
       "      <td>up</td>\n",
       "      <td>20744.298540</td>\n",
       "      <td>4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25244.298540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3410 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             日期  日增长率    涨跌          持有金额   剩余本金   操作           总资产\n",
       "0    2020-01-02  2.92    up      0.000000  20000  NaN  20000.000000\n",
       "1    2020-01-03  0.51    up      0.000000  20000  NaN  20000.000000\n",
       "2    2020-01-06  1.98    up      0.000000  20000  NaN  20000.000000\n",
       "3    2020-01-07  0.17    up      0.000000  20000  NaN  20000.000000\n",
       "4    2020-01-08 -1.16  down      0.000000  20000  NaN  20000.000000\n",
       "..          ...   ...   ...           ...    ...  ...           ...\n",
       "336  2021-05-26 -0.12  down  20372.810590   4700  NaN  25072.810590\n",
       "337  2021-05-27  0.66    up  20507.271140   4700  buy  25207.271140\n",
       "338  2021-05-28 -0.30  down  20545.329687   4600  NaN  25145.329687\n",
       "339  2021-05-31  0.06    up  20557.656885   4600  buy  25157.656885\n",
       "340  2021-06-01  0.42    up  20744.298540   4500  NaN  25244.298540\n",
       "\n",
       "[3410 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 96
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 1-2 数据创建\n",
    "\n",
    "<br>\n",
    "\n",
    "除了直接读取本地文件，学会直接创建数据框也很重要，常见于测试一些函数，下面是从常见数据结构创建数据框的方法整理"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 25 从列表创建\r\n",
    "\r\n",
    "<br>\r\n",
    "\r\n",
    "将下面的 `list` 转换为 `dataframe`，并指定列名为`\"早起Python\"`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "source": [
    "l = [1,2,3,4,5]\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "source": [
    "data = pd.DataFrame({\"早起Python\": l})\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>早起Python</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   早起Python\n",
       "0         1\n",
       "1         2\n",
       "2         3\n",
       "3         4\n",
       "4         5"
      ]
     },
     "metadata": {},
     "execution_count": 100
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 26 从列表创建｜嵌套列表\n",
    "\n",
    "<br>\n",
    "\n",
    "将下面的 `list` 转换为 `dataframe`，并指定行索引为`\"公众号\",\"早起Python\"`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "source": [
    "l = [[1,2,3],[4,5,6]]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "source": [
    "data = pd.DataFrame({\"公众号\": l[0], \"早起Python\": l[1]})\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>公众号</th>\n",
       "      <th>早起Python</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   公众号  早起Python\n",
       "0    1         4\n",
       "1    2         5\n",
       "2    3         6"
      ]
     },
     "metadata": {},
     "execution_count": 106
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "![公众号：早起Python](http://liuzaoqi.oss-cn-beijing.aliyuncs.com/2021/09/18/16319660121648.jpg?域名/sample.jpg?x-oss-process=style/stylename)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 27 从字典创建"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "执行下方代码，并将字典转换为`dataframe`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "source": [
    "d = {\r\n",
    "    \"one\": pd.Series([1.0, 2.0, 3.0], index=[\"a\", \"b\", \"c\"]),\r\n",
    "    \"two\": pd.Series([1.0, 2.0, 3.0, 4.0], index=[\"a\", \"b\", \"c\", \"d\"]) }"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "source": [
    "data = pd.DataFrame(d)\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a  1.0  1.0\n",
       "b  2.0  2.0\n",
       "c  3.0  3.0\n",
       "d  NaN  4.0"
      ]
     },
     "metadata": {},
     "execution_count": 104
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 28 从字典创建｜指定索引\n",
    "\n",
    "<br>\n",
    "\n",
    "还是上一题的字典`d`，将其转换为`dataframe`并指定索引顺序为 `d、b、a`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "source": [
    "data = pd.DataFrame(d, index=['d','b','a'])\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "d  NaN  4.0\n",
       "b  2.0  2.0\n",
       "a  1.0  1.0"
      ]
     },
     "metadata": {},
     "execution_count": 116
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 29 从字典创建｜指定列名\n",
    "\n",
    "<br>\n",
    "\n",
    "还是上一题的字典`d`，将其转换为`dataframe`并指定索引顺序为 `d、b、a`，列名为`\"two\", \"three\"`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "source": [
    "data = pd.DataFrame(d, index=['d','b','a'], columns=['two', 'three'])\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   two three\n",
       "d  4.0   NaN\n",
       "b  2.0   NaN\n",
       "a  1.0   NaN"
      ]
     },
     "metadata": {},
     "execution_count": 123
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 30 从字典创建｜字典列表\n",
    "<br>\n",
    "\n",
    "将下方列表型字典转换为`dataframe`\n",
    "\n",
    "思考：如何指定行/列索引？"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "source": [
    "d = [{\"a\": 1, \"b\": 2}, {\"a\": 5, \"b\": 10, \"c\": 20}]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "source": [
    "data = pd.DataFrame(d)\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b     c\n",
       "0  1   2   NaN\n",
       "1  5  10  20.0"
      ]
     },
     "metadata": {},
     "execution_count": 127
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 31 从集合创建\n",
    "\n",
    "<br>\n",
    "\n",
    "将下面的元组转换为 dataframe 且行列索引均为 `1,2,3,4`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "source": [
    "t =((1,0,0,0,),(2,3,0,0,),(4,5,6,0,),(7,8,9,10,))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "source": [
    "data = pd.DataFrame(t, index=[1,2,3,4])\r\n",
    "data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2   3\n",
       "1  1  0  0   0\n",
       "2  2  3  0   0\n",
       "3  4  5  6   0\n",
       "4  7  8  9  10"
      ]
     },
     "metadata": {},
     "execution_count": 131
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 1-3 数据存储"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 32 保存为 CSV\n",
    "\n",
    "<br>\n",
    "\n",
    "将第三题读取到的数据保存为 `csv` 格式至当前目录下（文件名任意）"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "source": [
    "data = pd.read_csv(\"某招聘网站数据.csv\",nrows = 20)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "source": [
    "data.to_csv(\"./ttt.csv\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 33 保存为 CSV｜指定列\n",
    "\n",
    "<br>\n",
    "\n",
    "将第三题读取到的数据保存为 `csv` 格式至当前目录下（文件名任意），且只保留`positionName、salary`两列"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "source": [
    "data.to_csv(\"./ttt.csv\", columns=['positionName','salary'])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 34 保存为 CSV｜取消索引\n",
    "\n",
    "<br>\n",
    "\n",
    "将第三题读取到的数据保存为 `csv` 格式至当前目录下（文件名任意），且取消每一行的索引"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "source": [
    "data.to_csv(\"./ttt.csv\", index=None)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 35 保存为 CSV｜标记缺失值\n",
    "\n",
    "<br>\n",
    "\n",
    "在上一题的基础上，在保存的同时，将缺失值标记为`'数据缺失'`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "source": [
    "data.to_csv(\"./ttt.csv\", na_rep='数据缺失')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 36 保存为CSV｜压缩\n",
    "\n",
    "<br>\n",
    "\n",
    "将上一题的数据保存至 `zip` 文件，解压后出现 `out.csv`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 37 保存为 Excel \n",
    "\n",
    "<br>\n",
    "\n",
    "将第三题读取到的数据保存为 `xlsx` 格式至当前目录下（文件名任意）"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "source": [
    "data.to_excel('./ttt.xlsx')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 38 保存为 JSON\n",
    "\n",
    "将之前的数据保存为 `json` 格式至当前目录下（文件名任意）"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 39 保存为 Markdown"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "将之前数据转换为 `markdown` 形式表格，这样可以直接复制进 `.md` 文件中使用"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 40 保存为 Html\n",
    "\n",
    "将之前的数据保存为 `html` 格式至当前目录下（文件名任意），并进行如下设置\n",
    "- 取消行索引\n",
    "- 标题居中对齐\n",
    "- 列宽100"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "![](http://liuzaoqi.oss-cn-beijing.aliyuncs.com/2021/09/16/16317972442543.jpg?域名/sample.jpg?x-oss-process=style/stylename)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
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